Vercel Deploying

Automatically deploys to Vercel production, uses Vercel MCP to fetch build logs, analyzes errors, fixes them, and retries until successful deployment. Use when deploying to production or fixing deployment issues.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/vercel-deploying-seanchiuai-applyai

SKILL.md

Vercel Production Deploy Loop

Instructions

When requested to deploy to Vercel production with automatic error fixing:

Initial Deployment Attempt
- Run vercel --prod to start production deployment
- Wait for deployment to complete
Error Detection & Analysis
- CRITICAL: Use Vercel MCP tool to fetch detailed logs:
  - The MCP logs provide much more detail than CLI output
- Analyze the build logs to identify root cause:
  - Build errors (TypeScript, ESLint, compilation)
  - Runtime errors
  - Environment variable issues
  - Dependency problems
  - Configuration issues
- Extract specific error messages
Error Fixing
- Make minimal, targeted fixes to resolve the specific error
Retry Deployment
- Run vercel --prod again with the fixes applied
- Repeat steps until deployment succeeds
Success Confirmation
- Once deployment succeeds, report:
  - Deployment URL
  - All errors that were fixed
  - Summary of changes made
- Ask if user wants to commit/push the fixes

Loop Exit Conditions

✅ Deployment succeeds
❌ SAME error occurs 5+ times (suggest manual intervention)
❌ User requests to stop

Best Practices

Make incremental fixes rather than large refactors
Preserve user's code style and patterns when fixing

Example Flow

User: "Deploy to production and fix any errors"

Vercel MCP build logs are the PRIMARY source of error information
CLI output alone is insufficient for proper error diagnosis
Always wait for deployment to complete before fetching logs
If errors require user input (like API keys), prompt user immediately